Stochastic Gravitational Wave Detection Nima Laal Oregon State - - PowerPoint PPT Presentation

Stochastic Gravitational Wave Detection

Nima Laal Oregon State University NANOGrav Collaboration

Artwork by Sandbox Studio, Chicago with Corinne Mucha Taken from symmetrymagazine.org

Stochastic Gravitational Wave Background (SGWB)

• Sources are:

○ isotropic ○ independent ○ point-like ○ many ○ far away

• Gravitational waves from such

sources correlate photons’

• geodesics. Pulsar Timing

Array (PTA) is used to observe the correlations.

Animation by R. Hurt - Caltech / JPL

Credit: NASA/DOE/Fermi LAT Collaboration via Nature

The Problem of Detection

Stochastic gravitational wave behaves like noise in a PTA data set; however, it is not the

• nly source of noise. So, how

to tell if a noise is SGWB?

You look for this Hellings and Downs curve, which is hard to extract from a PTA data, but it is THE definite proof for existence of SGWB.

The First Step: Noise Analysis

The easiest way to distinguish noises from each other is through their power spectral density. The Powerlaw Model:

Spectral index Power Amplitude Reference Frequency Frequency

Colored Noise

Terminology

• The most common colored

noises in a PTA data set are:

○ Red: any noise with positive spectral index ○ White: any noise with zero spectral index

A Toy Model

A pulsar with only one white,

• ne red, and one SGWB

component and all deterministic signals removed

All surviving signals are assumed to be random noises following a powerlaw spectral density model with SGWB noise having a spectral index of ! = 13/3 (red noise).

Data = GW + Red Noise + White Noise White noise dominates at high frequencies Red noise could dominate at low frequencies

You see the problem? Not only the “SGWB” is weak, it is also hidden by high white noise signal. In addition, it is not deterministic!

In reality…

• SGWB is Red, and that is a

problem!

• Deterministic signals need to

be removed

○ spin down period, ephemeris variation, pulsar sky location variation, equipment change,…

• Stochastic signals need to be

understood and well modeled

○ SGWB, receiver noise, clock noise, interstellar medium fluctuations, …

• Our models become

computationally expensive

So, how do we do the noise analysis?

• We simply wait long enough

(so far 15 years) for the red noises to dominate the white noises (at least in low frequencies)

• We focus more on the lower

frequency bins of our data.

• While waiting, we constantly

improve the effectiveness of

• ur Bayesian models in

detecting any trace of a Red noise process that can potentially be a SGWB.

We Detect!

Credit: NANOGrav 11 Year and 12.5 Year (draft) papers